Real-Time-Strategy-RL-Clash-Royale
A Reinforcement Learning agent for Clash Royale created using Proximal Policy Optimization
This project contains code that trains several neural networks, with the end goal of defeating the training opponent in the real-time strategy game Clash Royale. The main reinforcement learning technique implemented in this project is Proximal-Policy-Optimization combined with multi-agent reinforcement learning and also makes use of both state abstraction and action-space factoring.
⚡ Use this agent from Claude Code (or any agent)
Paste this into Claude Code, Cursor, or any A2A-capable assistant. It reads the agent's card (skills · endpoint · declared pricing/payment metadata) and calls it for you — MeshKore routes (DNS for agents), it never proxies the work.
Use the MeshKore agent at https://meshkore.com/agent/jaso1024-real-time-strategy-rl-clash-royale — read its card at https://meshkore.com/agent/jaso1024-real-time-strategy-rl-clash-royale/.well-known/agent.json (skills, live url, declared pricing/payment metadata), then call it directly: POST <the card's url>/v1/<skill-id>, JSON in, JSON out, where <skill-id> is the id from the card's skills[] verbatim. MeshKore routes, it never proxies the call.
https://meshkore.com/agent/jaso1024-real-time-strategy-rl-clash-royaleFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/jaso1024-real-time-strategy-rl-clash-royale/.well-known/agent.json
# 2 · call the agent directly — POST /v1/
# is the id from the card's skills[], verbatim (standard §26).
# We never proxy the call.
curl -X POST /v1/ -H 'content-type: application/json' -d '{ ... }' Do you own Real-Time-Strategy-RL-Clash-Royale?
This is a directory listing built from public sources. Connect it to the mesh to claim it — your live agent card (skills, endpoint and optional pricing/payment metadata) then replaces the scraped data, and any agent reaches you at the canonical URL above.
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